For many years, conventional radiography employed photosensitive silver-halide film medium for recording images of human and animal tissue. Then, in order to take advantage of advances in digital image processing, many radiographic images originally recorded on film were digitized in order to provide these images in digital data format. This image data could then be analyzed by sophisticated imaging algorithms capable of detecting various conditions that might not be easily be discerned from the film image, even by the trained eye of a skilled diagnostician. A considerable amount of research and development effort has been expended over the last several years to develop and refine image processing algorithms that assist the diagnostician in assessing digitized images. These methods include algorithms capable of detecting conditions evidenced by very subtle effects in an image, such as in mammography and bone-marrow density (BMD) radiography.
More recent advances in imaging technology have made it possible to obtain radiographic images directly as digital data, without the use of photosensitive film. Digital imaging can be performed using Computed Radiography (CR) apparatus that scans and records image data on an erasable sheet of stimulable storage phosphors or using direct Digital Radiography (DR) that obtains image data directly from radiation received from a stimulable storage phosphor. Digital imaging apparatus of these types are particularly advantaged for their wider dynamic range over conventional film imaging. These different film, CR, and DR imaging modalities differ from each other due the different imaging technologies used. Moreover, even within the same imaging modality, there can be differences in results between systems provided by different equipment manufacturers.
Although digital imaging techniques enjoy some advantages over earlier film-based imaging, there are some drawbacks. One of these drawbacks relates to differences in sensitometric response between photosensitive film that is scanned and digitized and the receiver media that are used for obtaining digital data using CR or DR methods. Sensitometric response for a radiographic imaging system is defined in terms of the amount of output signal that is obtained for a given amount of radiation. This sensitometric response for film and digital systems differs significantly in how it is expressed in terms of the output signal level.
For conventional film-based radiography, sensitometric response is plotted as a curve relating Density to the log Exposure. FIG. 1 shows the characteristic sensitometric response of conventional photosensitive film, such as that used in X-ray imaging. This familiar “sigmoid” relationship of the log of incident radiation to the optical density is well known to those skilled in the imaging arts. The schematic diagrams of FIGS. 2A and 2B show what happens in a more general sense. For conventional photosensitive film, the sensitometric response relates the log of the amount of radiation received (conventionally plotted along the abscissa or x-axis of the graph) with the optical density or signal value that is obtained (conventionally plotted along the ordinate or y-axis of the graph). As shown again in FIG. 2A, film shows a sigmoid response curve, wherein the signal value relates to optical density (OD). As shown in FIG. 2B, digital modalities typically exhibit some other characteristic response, including a more linear response when plotted against a value of the incident radiation, and provide an altogether different type of signal value.
As a result of this difference between film and digital systems with respect to what can be considered “signal space” or “recording space”, algorithms that were originally developed and fine-tuned for scanned and digitized image data (that is, data obtained from scanned film and exhibiting the sigmoid sensitometric response of film) require some transformation of image data obtained from a CR or DR receiver. Considered more generally, film and digital signal spaces are not identical. Utilities and tools that are developed for one type of imaging modality often do not perform well when used with images of some other type. Thus, the potential value of these diagnostic image analysis tools, developed and perfected for scanned film signal space over years of effort and ongoing research, can be diluted or even lost with the transition to digital receivers. Thus, for example, the same algorithm that automatically detects a lesion or other problem condition from scanned film data is unusable for CR or DR receiver data.
There is, then, a need for a method that provides signal space mapping between the image data obtained using different imaging modalities, effectively converting image data between various types. Suitable methods are needed not only for data transformation between film and digital receiver types, but also between digital receiver types themselves, and even between the same types of digital receivers provided from different systems and manufacturers. Given more accurate signal space mapping, image analysis algorithms and tools that were originally developed and trained for application to data in one imaging modality can be readily used, without significant adjustments, with data from an alternate imaging modality.